First cover the core stack: SQL (joins, GROUP BY, subqueries, window functions), Excel (VLOOKUP/XLOOKUP, pivot tables), one BI tool like Power BI, and basic statistics. Then prepare 2–3 projects you can explain end to end, go through real interview experiences from Indian companies to understand question patterns, rehearse common HR answers like "why analytics" and "tell me about yourself," and practise writing SQL live. Freshers who explain their projects confidently and solve a live query usually stand out far more than candidates with only certificates.

Follow a structured 6–8 week plan: weeks 1–2 for Excel and basic statistics, weeks 3–4 for SQL, weeks 5–6 for Power BI along with building 1–2 dashboards, and weeks 7–8 for mock interviews and revision from question banks of real interview questions and answers. Maintain a personal answer sheet, and do at least 2–3 mock interviews. Random tutorial-hopping is the most common mistake — a clear plan with daily practice beats months of unstructured learning.

Good sources include the interview sections on job and company review portals, LinkedIn posts by working analysts, and curated PDFs and question banks designed for freshers — including packs that compile 70+ real data analyst interview experiences with 1500+ questions and answers. Don't just memorise answers though; understand the reasoning behind each one and practise saying your answers out loud. Fresher-specific compilations covering SQL, Excel, Power BI, statistics and the HR round save a lot of scattered searching.

At the 3-year level, interviewers test depth over definitions: complex SQL with window functions and query optimisation, scenario-based questions like "how would you measure the success of a campaign," data modelling decisions, dashboard performance tuning, and stakeholder-handling situations. You should be able to quantify the business impact of your past work — time saved, revenue influenced, processes improved. Preparation should shift from "what is X" to "how did you use X to solve a real problem," along with salary and team-fit discussions.

Pick 3–5 end-to-end projects on realistic business datasets — for example a sales performance dashboard, HR attrition analysis, e-commerce customer analysis, or a marketing campaign ROI report. Each project should clearly show the problem statement, data cleaning steps, SQL queries used, a dashboard with meaningful KPIs, and 3–4 business recommendations. Avoid tutorial clones since recruiters spot them instantly. Portfolio-ready project bundles (some include 45+ guided projects) can speed things up, but make sure you can explain every design choice in the interview.